P449: PROTEOGENOMIC PROFILING OF DIAGNOSTIC AND RELAPSE SAMPLES IN PEDIATRIC ACUTE MYELOID LEUKEMIA
Authors: Tina Glisovic-Aplenc, Jen Liddle, Lusha Cao, Kevin Nestler, Han Fisher, Asif Chinwalla, Hossein Fazelinia, Saar Gill, Yi Xing, Mingyao LI, Kathrin Bernt, Jeremy Balsbaugh, Richard Aplenc
Journal: HemaSphere (2023)

Background: Pediatric acute myeloid leukemia (AML) is the second most common pediatric leukemia but accounts for about 50% of pediatric leukemia-related mortality. While transcriptomics has identified novel pediatric AML subgroups, therapeutic targets and potential biologic mechanisms underlying treatment failure, only one proteomic array study has evaluated subsets of pediatric AML. Thus, to our knowledge, a comprehensive, integrated proteogenomic analysis of pediatric AML is lacking. Such an analysis could identify new therapeutic targets and drivers of treatment failure. Using 182 matched diagnosis/relapse samples from 91 patients on the Children’s Oncology Group AAML1031 trial, we developed a novel proteogenomic laboratory and data analysis workflow to identify drivers of pediatric AML recurrence and new therapeutic targets. Aims: Identify proteins and biological pathways that underly AML relapse in children with newly diagnosed AML, and proteins uniquely/differentially expressed between diagnostic AML samples and healthy donors to identify immunotherapy targets. Methods: Total protein, gDNA and RNA extractions were performed by a third party vendor using the Cytiva TriplePrep kit. Protein samples were processed with the S-trap 96-well plate protocol prior to LC/MS data-independent acquisition (DIA). BSA, MOLM14 and pooled sample controls were included for quality control monitoring. Peptides were identified using Spectronaut (v.17). MS raw files were searched in directDIA mode against a combined human and bovine reference proteome. Protein level quantification was performed on unique peptide abundances. Proteins identified in > 30% of samples in a sample group were included in downstream analyses. Differential expression analysis was performed with the Limma package. Upregulated proteins were set as log2 fold change > 1 and FDR < 0.1 when comparing relapse with diagnosis. A log2 fold change > 2 and FDR < 0.05 was set when comparing diagnosis with healthy donors. Results: A total of 224 samples were analyzed: 182 diagnosis/relapse samples representing 91 patients, 16 healthy controls, 5 pooled samples, and 21 MOLM14. Patients included in these analyses were demographically representative of all AAML1031 patients. Due to technical limitations, patient protein samples were contaminated by fetal bovine serum from residual cryopreservation medium. Of 8329 total proteins identified 4% were bovine only, 9% shared human and bovine, and 87% human only proteins. 7037 proteins were included in downstream analyses. A total of 9 and 4 proteins were uniquely identified in the diagnosis and relapse samples, respectively. 63 proteins were significantly upregulated in the relapse samples compared to diagnosis, and 183 proteins were significantly downregulated (Figure 1A). A total of 66 and 14 proteins were uniquely identified in the diagnosis and healthy controls. 457 proteins were significantly upregulated and 739 proteins were significantly downregulated in the diagnosis samples relative to healthy donors (Figure 1B).Summary/Conclusion: To our knowledge, this is the first pediatric AML mass spectrometry-based proteomic study. This analytic approach identifies more than 7,000 human proteins representing multiple biological pathways and 66 potential novel immunotherapy targets. These data substantially extend the work by Hoff et al who profiled 500 AML diagnosis samples from AAML1031 using RPPA microarrays with 296 proteins (PMID:35021602). Work is ongoing to validate identified differentially expressed proteins and pathways and to integrate available RNA expression data. Keywords: Pediatric, Relapsed acute myeloid leukemia, Acute myeloid leukemia, Proteomics